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Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.
Adi, Wihan; Perez, Bryan E Rubio; Liu, Yuming; Runkle, Sydney; Eliceiri, Kevin W; Yesilkoy, Filiz.
Afiliación
  • Adi W; Department of Biomedical Engineering University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Perez BER; Department of Electrical and Computer Engineering University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Liu Y; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA.
  • Runkle S; Department of Computer Science University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Eliceiri KW; Department of Biomedical Engineering University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Yesilkoy F; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA.
bioRxiv ; 2024 May 26.
Article en En | MEDLINE | ID: mdl-38826188
ABSTRACT

Significance:

Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues.

Aim:

To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures.

Approach:

We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment.

Results:

Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment.

Conclusions:

We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos